An integrated modelling framework for building a daily river system model for the Murray–Darling Basin, Australia

References

• Ai-Jaroodi, J., et al., 2002. An agent-based infrastructure for parallel java on heterogeneous clusters. In: Proceedings of the IEEE international conference on cluster computing, 19–27. IEEE Computer Society. doi:10.1109/CLUSTR.2002.1137724
   Google Scholar
• Al-Jaroodi, J. and Mohamed, N., 2003. An object-passing model for parallel programming. In: Proceedings of the 27th annual international computer software and applications conference. COMPAC 2003, 138–143. IEEE Computer Society. doi:10.1109/CMPSAC.2003.1245333
   Google Scholar
• Bach, M. and Ostrowski, M., 2013. Analysis of intensively used catchments based on integrated modelling. Journal of Hydrology, 485 (2013), 148–161. doi: 10.1016/j.jhydrol.2012.07.001
   Web of Science ®Google Scholar
• Barker, A. and Van Hemert, J., 2008. Scientific workflow: a survey and research directions. Parallel processing and applied mathematics [online]. Available from: http://link.springer.com/chapter/10.1007/978-3-540-68111-3_78 [Accessed 11 Oct 2013].
   Google Scholar
• Bates, B.C., et al., 2008. Climate change and water. Technical paper of the intergovernmental panel on climate change, IPCC secretariat, Geneva, 210 pp.
   Google Scholar
• Becu, N., et al., 2003. Agent based simulation of a small catchment water management in northern Thailand: description of the CATCHSCAPE model. Ecological Modelling, 170 (2–3), 319–331. doi: 10.1016/S0304-3800(03)00236-9
   Web of Science ®Google Scholar
• Bhuiyan, J. and Dutta, D., 2012. Analysis of flood vulnerability and assessment of the impacts in coastal zones of Bangladesh due to potential sea-level rise. Natural Hazards, 61, 729–743. doi: 10.1007/s11069-011-0059-3
   Web of Science ®Google Scholar
• Bithell, M. and Brasington, J., 2009. Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution. Environmental Modelling & Software, 24 (2), 173–190. doi: 10.1016/j.envsoft.2008.06.016
   Web of Science ®Google Scholar
• Black, D., et al., 2011. Guidelines for water management modelling: towards best-practice model application. Canberra: eWater CRC.
   Google Scholar
• Boughton, W., 2004. The Australian water balance model. Environmental Modelling & Software, 19 (10), 943–956. doi: 10.1016/j.envsoft.2003.10.007
   Web of Science ®Google Scholar
• Bousquet, F. and Page, C., 2004. Multi-agent simulations and ecosystem management: a review. Ecological Modelling, 176 (3–4), 313–332. doi: 10.1016/j.ecolmodel.2004.01.011
   Web of Science ®Google Scholar
• Bulatewicz, T., et al., 2013. The simple script wrapper for OpenMI: enabling interdisciplinary modeling studies. Environmental Modelling & Software, 39, 283–294. doi: 10.1016/j.envsoft.2012.07.006
   Web of Science ®Google Scholar
• Burnash, R.J.C., Ferral, R.L., and McGuire, R.A., 1973. A generalised stream-flow simulation system: conceptual modelling for digital computers. Technical report, Joint Federal & State River Forecast Center, Sacramento.
   Google Scholar
• Carr, R. and Podger, G., 2012. eWater Source – Australia’s next generation IWRM modelling platform. 34th Hydrology and water resources symposium (December 2012). ISBN 978-1-922107-62-6.
   Google Scholar
• Castronova, A.M., Goodall, J.L., and Ercan, M.B., 2013. Integrated modeling within a hydrologic information system: an OpenMI based approach. Environmental Modelling & Software, 39, 263–273. doi: 10.1016/j.envsoft.2012.02.011
   Web of Science ®Google Scholar
• Chiew, F.H.S., Peel, M.C., and Western, A.W., 2002. Application and testing of the simple rainfall–runoff model SIMHYD. Littleton: Water Resources, 335–367.
   Google Scholar
• Chiew, F., et al., 2008. Rainfall–runoff modelling across the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. Canberra: CSIRO, 62 pp.
   Google Scholar
• Close, A.F., 1996. The BIGMOD model of flow in the River Murray. MDBC Technical Report 96/12. Canberra: Murray-Darling Basin Commission.
   Google Scholar
• Croke, B.F.W., et al., 2006. IHACRES classic plus: a redesign of the IHACRES rainfall–runoff model. Environmental Modelling & Software, 21, 426–427. doi: 10.1016/j.envsoft.2005.07.003
   Web of Science ®Google Scholar
• CSIRO, 2008. Water availability in the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Australia.
   Google Scholar
• Diment, G.A., 1991. Wide use of a generalised headworks and resources model: REALM. Proceedings International Hydrology and Water Resources Symposium. Perth:, Institution of Engineers, Australia, National Conference Publication, 91 (22), 579–583.
   Google Scholar
• Dunstan, N., et al., 2005. Multi-agent design for catchment analysis. In: International conference on information and communication technology. New York: IEEE, 101–107.
   Google Scholar
• Dutta, D. and Nakayama, K., 2009. Effects of spatial grid resolution on river flow and surface inundation simulation by physically based distributed modelling approach. Hydrological Processes, 23, 534–545. doi: 10.1002/hyp.7183
   Web of Science ®Google Scholar
• Dutta, D., et al., 2012a. A comparative evaluation of short-term streamflow forecasting using time series analysis and rainfall–runoff models in eWater Source. Water Resources Management, 26 (15), 4397–4415. doi: 10.1007/s11269-012-0151-9
   Web of Science ®Google Scholar
• Dutta, D., et al., 2012b. A daily river system model for the Murray-Darling basin: development, testing and implementation. In: Proceedings of the 34th hydrology & water resources symposium, Sydney, 1057–1066.
   Google Scholar
• Dutta, D., et al., 2013. A new modelling system for river operations with a case study from the Goulburn River, Australia. Journal of Environmental Management, 121, 13–28. doi: 10.1016/j.jenvman.2013.02.028
   PubMed Web of Science ®Google Scholar
• Dutta, D., et al., 2014. Examining two different approaches to modelling management and operation rules in a Single River System Model. Water Resources Management, 28, 1713–1732. doi: 10.1007/s11269-014-0583-5
   Web of Science ®Google Scholar
• Dutta, D., et al., 2017. Development and application and of a large scale river system model for national water accounting in Australia. Journal of Hydrology, 547, 124–142. doi: 10.1016/j.jhydrol.2017.01.040
   Web of Science ®Google Scholar
• Esposito, A. and Loia, V., 2000. Integrating concurrency control and distributed data into workflow frameworks: An actor model perspective. In: SMC 2000 conference proceedings. 2000 IEEE international conference on systems, man and cybernetics. ‘Cybernetics evolving to systems, humans, organizations, and their complex interactions’ (Cat. No. 00CH37166), 3:2110-14. IEEE. doi:10.1109/ICSMC.2000.886427.
   Google Scholar
• eWater, 2014a. eWater Source –Australia’s national hydrological modelling platform, eWater, University of Canberra, ACT [online]. Available from: http://www.ewater.com.au/products/ewater-source/ [Accessed 15 Nov 2014].
   Google Scholar
• eWater, 2014b. eWater Source and Toolkit News – July 2014, eWater, University of Canberra, ACT, http://www.ewater.com.au/newsletter/Source-Toolkit-news-14-07-16.html
   Google Scholar
• Farolfi, S., Muller, J.-P., and Bonte, B., 2010. An iterative construction of multi-agent models to represent water supply and demand dynamics at the catchment level. Environmental Modelling & Software, 25 (10), 1130–1148. doi: 10.1016/j.envsoft.2010.03.018
   Web of Science ®Google Scholar
• Granell, C., Schade, S., and Ostländer, N., 2013. Seeing the forest through the trees: a review of integrated environmental modelling tools. Computers, Environment and Urban Systems, 41, 136–150. doi: 10.1016/j.compenvurbsys.2013.06.001
   Web of Science ®Google Scholar
• Gregersen, J.B., Gijsbers, P.J.A., and Westen, S.J.P., 2007. OpenMI: open modelling interface. Journal of Hydroinformatics, 9 (3), 175–191. doi: 10.2166/hydro.2007.023
   Web of Science ®Google Scholar
• Hughes, J., et al., 2014. An automated multi-step calibration procedure for a river system model. Environmental Modelling & Software, 51, 173–183. doi: 10.1016/j.envsoft.2013.09.024
   Web of Science ®Google Scholar
• Janssen, S., et al., 2011. Linking models for assessing agricultural land use change. Computers and Electronics in Agriculture, 76 (2), 148–160. doi: 10.1016/j.compag.2010.10.011
   Web of Science ®Google Scholar
• Jeffrey, S.J., et al., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling and Software, 16, 309–330. doi: 10.1016/S1364-8152(01)00008-1
   Web of Science ®Google Scholar
• Kabir, M.A., Dutta, D., and Hironaka, S., 2011. Process-based distributed modeling approach for analysis of sediment dynamics in a river basin. Hydrology and Earth System Sciences, 15 (4), 1307–1321. doi: 10.5194/hess-15-1307-2011
   Web of Science ®Google Scholar
• Kelly (Letcher), R.A., et al., 2013. Selecting among five common modelling approaches for integrated environmental assessment and management. Environmental Modelling & Software, 47, 159–181. doi: 10.1016/j.envsoft.2013.05.005
   Web of Science ®Google Scholar
• Knapen, R., et al., 2013. Evaluating OpenMI as a model integration platform across disciplines. Environmental Modelling & Software, 39, 274–282. doi: 10.1016/j.envsoft.2012.06.011
   Web of Science ®Google Scholar
• Lerat, J., et al., 2013. Calibration of river models for the Flinders and Gilbert catchments. A technical report to the Australian Government from the CSIRO flinders and gilbert agricultural resource assessment, part of the North Queensland irrigated agriculture strategy. CSIRO water for a healthy country and sustainable agriculture flagships, Australia.
   Google Scholar
• Murray-Darling Basin Authority, 2009. Development of sustainable diversion limits for the Murray-Darling Basin, issues paper, November, MDBA publication no. 48/09.
   Google Scholar
• Murray-Darling Basin Commission, 2002. Setting up of MSM_BIGMOD modelling suite for the River Murray System. MDBC Technical Report 2002/5.
   Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi: 10.1016/0022-1694(70)90255-6
   Google Scholar
• Perera, B.J.C., James, B., and Kularathna, M.D.U., 2005. Computer software tool REALM for sustainable water allocation and management. Journal of Environmental Management, 77, 291–300. doi: 10.1016/j.jenvman.2005.06.014
   PubMed Web of Science ®Google Scholar
• Perrin, C., Michael, C., and Andreassian, V., 2003. Improvement of a parsimonious model for streamflow simulations. Journal of Hydrology, 279, 275–289. doi: 10.1016/S0022-1694(03)00225-7
   Web of Science ®Google Scholar
• Podger, G., 2004. IQQM reference manual. Software version 7.32. New South Wales Department of Infrastructure Planning and Natural Resources.
   Google Scholar
• Podger, G. and Beecham, R., 2004. IQQM user manual. New South Wales Department of Infrastructure Planning and Natural Resources.
   Google Scholar
• Reaney, S.M., 2008. The use of agent based modelling techniques in hydrology: determining the spatial and temporal origin of channel flow in semi-arid catchments. Earth Surface Processes and Landforms, 33 (2), 317–327. doi: 10.1002/esp.1540
   Web of Science ®Google Scholar
• Ryan, J.G. and McAlpine, C.A., 2005. Simulating the feedback between land cover configuration and ecohydrological functioning in complex adaptive landscapes. MODSIM 2005: international Congress on modelling and simulation: advances and applications for management and decision making. Nedlands: University of Western Australia, 758–764.
   Google Scholar
• Schreinemachers, P. and Berger, T., 2008. Multi-agent systems for the simulation of land use change and policy interventions, modelling and simulation (Giuseppe Petrone and Giuliano Cammarata, eds.). ISBN: 978-3-902613-25-7, InTech. doi:10.5772/5989.
   Google Scholar
• Shrestha, N.K., et al., 2013. OpenMI-based integrated sediment transport modelling of the River Zenne, Belgium. Environmental Modelling & Software, 47, 193–206. doi: 10.1016/j.envsoft.2013.05.004
   Web of Science ®Google Scholar
• Simons, M., Podger, G., and Cooke, R., 1996. IQQM – A hydrologic modelling tool for water resource and salinity management. Environmental Software, 11 (1–3), 185–192. doi: 10.1016/S0266-9838(96)00019-6
   Google Scholar
• Tillman, T., et al., 2001. Interaction analysis of stakeholders in water supply systems. Water Science and Technology, 43 (5), 319–326.
   PubMed Web of Science ®Google Scholar
• van Oel, P.R., et al., 2010. Feedback mechanisms between water availability and water use in a semi-arid river basin: a spatially explicit multi-agent simulation approach. Environmental Modelling & Software, 25 (4), 433–443. doi: 10.1016/j.envsoft.2009.10.018
   Web of Science ®Google Scholar
• Vaze, J., et al., 2004. Modelling the effects of landuse change on water and salt delivery from a catchment affected by dryland salinity in south-east Australia. Hydrological Processes, 18, 1613–1637. doi: 10.1002/hyp.1409
   Web of Science ®Google Scholar
• Vaze, J., et al., 2010. Rainfall–runoff modelling across southeast Australia: datasets, models and results. Australian Journal of Water Resources, 14 (2), 101–116.
   Google Scholar
• Vaze, J., et al., 2011a. Impact of climate change on water availability in the Macquarie–Castlereagh River Basin in Australia. Hydrological Processes, 25 (16), 2597–2612. doi: 10.1002/hyp.8030
   Web of Science ®Google Scholar
• Vaze, J., et al., 2011b. Guidelines for rainfall–runoff modelling: towards best practice model application [online]. ISBN 978-1-921543-51-7. Available from: http://www.ewater.com.au/uploads/files/eWater-Guidelines-RRM-(v1_0-Interim-Dec-2011).pdf [Accessed 11 Oct 2013].
   Google Scholar
• Vaze, J., et al., 2011c. Conceptual rainfall–runoff model performance with different spatial rainfall inputs. Journal of Hydrometeorology, 12 (5), 1100–1112. doi:10.1175/2011JHM1340.1.
   Web of Science ®Google Scholar
• Viney, N.R., et al., 2009. The usefulness of bias constraints in model calibration for regionalisation to ungauged catchments. In: 18th world IMACS/MODSIM Congress, 13–17 July 2009, Cairns, Australia, 3421–3427.
   Google Scholar
• Welsh, W., et al., 2013. An integrated modelling framework for regulated river systems. Environmental Modelling & Software, 39, 81–102. doi: 10.1016/j.envsoft.2012.02.022
   Web of Science ®Google Scholar
• Willmott, C.J., 1981. On the validation of models. Physical Geography, 2, 184–194.
   Google Scholar
• Wojda, P. and Brouyère, S., 2013. An object-oriented hydrogeological data model for groundwater projects. Environmental Modelling & Software, 43, 109–123. doi: 10.1016/j.envsoft.2013.01.015
   Web of Science ®Google Scholar
• Yang, A., 2010. The integrated river system modelling framework. A report to the Murray-Darling Basin authority. In: CSIRO: water for a healthy country national research flagship, June 2010, Canberra.
   Google Scholar
• Yang, A. and Podger, G., 2010. An agent based hydrological simulation system for Murray-Darling Basin, Australia. In: 9th international conference on hydroinformatics HIC 2010, 7–11 September 2010, Tianjin, 1478–1485.
   Google Scholar
• Yang, A., Abbass, H.A., and Sarker, R., 2006. Characterizing warfare in red teaming. IEEE Transactions on Systems, Man, Cybernetics, Part B: Cybernetics, 36 (2), 268–285. doi: 10.1109/TSMCB.2005.855569
   PubMed Web of Science ®Google Scholar
• Yang, A., et al., 2013. A river system modelling platform for Murray-Darling Basin, Australia. Journal of Hydroinformatics, 15 (4), 1109–1120. doi: 10.2166/hydro.2012.153
   Web of Science ®Google Scholar
• Zagona, E.A., et al., 2001. Riverware: a generalized tool for complex reservoir system modelling. Journal of the American Water Resources Association, 37 (4), 913–929. doi: 10.1111/j.1752-1688.2001.tb05522.x
   Web of Science ®Google Scholar
• Zhang, S., Xia, Z., and Wang, T., 2013. A real-time interactive simulation framework for watershed decision making using numerical models and virtual environment. Journal of Hydrology, 493 (17), 95–104. doi: 10.1016/j.jhydrol.2013.04.030
   Web of Science ®Google Scholar